MRI body or body relevant region image quality is affected by patient respiration motion. Respiration related MR imaging artifacts occur if they are not suppressed or reduced, which leads to ghosting that may overlay a region of interest or blurring which reduces image quality for clinical diagnosis. Respiration related patient physical motion is unavoidable during MR imaging, especially for body region imaging, such as of the abdomen, spine, pelvis and heart. Respiration inspiration and expiration, is a slow physical motion with frequencies ranging around 60 Hz. During a respiration cycle, the physical position of many organs, such as of a heart, liver or diaphragm changes resulting in inconsistency in collected MR data (phase shifts) during MR data acquisition. In addition to organ physical displacement, respiration also leads to known pseudo-periodic scanner base magnetic field (B0) variations that may cause phase variations in the acquired data. Due to these data inconsistencies, reconstructed MR images are normally contaminated with ghosting, blurring and other artifacts as shown in FIG. 1. FIG. 1A shows an image indicating respiratory motion related artifacts and FIG. 1B shows a corresponding comparison image without respiratory motion artifacts providing improved diagnosis information.
A known system reduces respiratory motion related artifacts by asking a patient to hold his breath (breath-hold=BH) while collecting data (scanning). For a healthy patient breath-hold typically varies from 15 to 30 seconds depending on individual personal conditions. However, for many patients, a long breathhold may be impossible to achieve, depending on patient physical condition, age and health. Consequently known systems employ fast imaging methods including shortened TR (time between successive RF excitation pulses) using a fast magnetic field gradient (HW), applying parallel imaging with multi-receiving coils and parallel data processing methods such as SENSE ((SENSitivity Encoding) and GRAPPA (Generalized autocalibrating partially parallel acquisitions (GRAPPA).
Another known system uses gating to synchronize MR data acquisition with a pseudo respiration cycle limiting data acquisition to periods when organs are in a certain position. This method allows patients free respiration without breathhold using an external motion sensor (e.g., a respiration belt) or an internal MR navigator image acquisition method to track respiration motion during data acquisition, and trigger data collection when organs are in a certain position. This method is relatively inefficient resulting in prolonged scanning time and increase in patient discomfort. This method also typically results in variable image contrast due to long and variable repetition time (TR). The use of segmented acquisition improves data collection efficiency at the cost of image quality reduction by allowing organ motion within a certain range but the scanning time may still be too long for some patients.
FIG. 2 shows a known system for Motion Artifact Removal by Retrospective Resolution Reduction (MARs) using a breathhold strategy. Clean k-space image data without motion error is collected at the beginning of breath-hold as reference to distinguish the data collected in a later phase of breath-hold, potentially affected by physical motion. Correlation function 207 determines correlation between image representative data from parallel RF coils 203, 205, to derive a Motion Artifact Removal parameter 209 which is averaged and compared against a threshold in unit 211 for determining an indicator of motion artifact used for identifying corrupted k-space data lines. The identified corrupted k-space data lines are zero filled in unit 215. Images 220 and 222 are images with and without MARs correction respectively. The known MARs system collects the most important data, e.g. the center of k-space, at the beginning while patients can still hold their breath and collects less significant data (high k-space data) later. At the end of the data acquisition, the data with respiration motion contaminated data is discarded to remove the artifacts by sacrificing some resolution. For example, suppose a requested breathhold is 15 seconds and after 10 seconds the patient cannot maintain breathhold, the last 5 seconds of MR data is contaminated with motion. However, this data is less significant as it is outside the k-space center and can be discarded as a trade-off between artifact-free image data and image resolution and thus achieve an improved clinical diagnosis image. Selection and discarding “bad” data during a late phase of breathhold is important in this method and reliability of the known system is largely dependent on the acquired signal quality, i.e. signal-noise-ratio (SNR) and the method is vulnerable to a poor SNR which is common for non-central k-space data. A system according to invention principles addresses these deficiencies and related problems.